1. Field of the Invention
The present invention generally relates to systems and methods for detecting and classifying defects on semi-conductor wafers based on one or more attributes determined from a standard reference image.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices such as ICs. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
A frequently-used inspection algorithm is multi-die automatic thresholding (MDAT). It calculates a difference value between test and reference images at each pixel. Two values, the difference and gray level computed from the reference image, at all pixels in an image are used to construct a two-dimensional (2D) histogram. The horizontal axis (also called the detection axis) represents values of differences between test and references. The vertical axis (also called the segmentation axis) represents values of gray levels constructed from images of neighboring dies. The user can divide the values along the vertical axis into multiple segments and specify different thresholds for values in the horizontal axis. Pixels having larger difference values than the threshold values are considered to be defective pixels by MDAT. Due noise and process variation, the gray level distribution in the vertical axis may be different from one die to another die. Pixels belonging to one segment in one die may belong to another segment on another die. This problem causes unstable inspection and inconsistent inspection results. Some defect attributes calculated from reference images are also different due to variation of the gray level value distribution. Thus, the defect classification based these attributes is affected.
FIG. 9 illustrates the problem with the existing MDAT algorithm described above. In particular, FIG. 9 illustrates two 2D histograms generated with the horizontal axis representing the difference values described above and the segmentation axis representing the values of the gray levels described above. One of the histograms is, as shown in FIG. 9, a 2D histogram used to determine recipe parameters. The other histogram is, as shown in FIG. 9, a 2D histogram generated for die M on wafer 900. The lines shown in FIG. 9 by a combination of dashes and dots show the segment break and thresholds for each segment in the recipe. They are determined based on some data (e.g., from different areas, different wafers, etc.). However, if there is some change in image gray levels in different areas such as die M, the 2D histogram location and shape are different from the ones used for recipe setup. The solid straight lines shown in FIG. 9 show the ideal segment break and threshold for die M. The actual recipe parameters (shown by the lines made up of a combination of dashes and dots) are shifted and cause reduced inspection sensitivity. Due to wafer noise and color variation, 2D histograms for different locations could vary. Therefore, it would be advantageous to stabilize the 2D histogram location so that segmentation breaks can work across wafers and between wafers.
Some inspection methods use standard images such as standard die images to detect defects on wafers. This approach is used to qualify photo masks or reticles. If there is a defect on a single-die reticle, the defect will be printed on every die. A normal die-to-die comparison algorithm does not have good sensitivity because subtraction of two of the same defects between dies does not indicate a large difference. In order to detect this type of defect, a reference image without die-repeater defects is needed. For example, a standard die image (also commonly referred to as a “golden die” or “standard reference die”) may be compared to a test die image acquired for a wafer being inspected and the results of the comparison may be input to a defect detection algorithm or method to determine if any defects are present in the test die. Such golden die images are commonly generated from a clean wafer or a few clean dies where there is no die repeater defects or die-repeaters are removed from images of the dies. This golden die image is compared to images of all dies for all wafers.
Accordingly, it would be advantageous to develop systems and methods for detecting and classifying defects on a wafer that do not have one or more of the disadvantages described above.